This lesson provides a conceptual overview of the rudiments of machine learning, including its bases in traditional statistics and the types of questions it might be applied to. The lesson was presented in the context of the BrainHack School 2020.

This lesson provides a hands-on, Jupyter-notebook-based tutorial to apply machine learning in Python to brain-imaging data.

This lesson presents advanced machine learning algorithms for neuroimaging, while addressing some real-world considerations related to data size and type.

This lesson from freeCodeCamp introduces Scikit-learn, the most widely used machine learning Python library.

In this lecture, attendees will learn about the opportunities and challenges associated with Recurrent Neural Networks (RNNs), which, when trained with machine learning techniques on cognitive tasks, have become a widely accepted tool for neuroscientists.

This book was written with the goal of introducing researchers and students in a variety of research fields to the intersection of data science and neuroimaging. This book reflects our own experience of doing research at the intersection of data science and neuroimaging and it is based on our experience working with students and collaborators who come from a variety of backgrounds and have a variety of reasons for wanting to use data science approaches in their work. The tools and ideas that we chose to write about are all tools and ideas that we have used in some way in our own research. Many of them are tools that we use on a daily basis in our work. This was important to us for a few reasons: the first is that we want to teach people things that we ourselves find useful. Second, it allowed us to write the book with a focus on solving specific analysis tasks. For example, in many of the chapters you will see that we walk you through ideas while implementing them in code, and with data. We believe that this is a good way to learn about data analysis, because it provides a connecting thread from scientific questions through the data and its representation to implementing specific answers to these questions. Finally, we find these ideas compelling and fruitful. That’s why we were drawn to them in the first place. We hope that our enthusiasm about the ideas and tools described in this book will be infectious enough to convince the readers of their value.

Maximize Your Research With Cloud Workspaces is a talk aimed at researchers who are looking for innovative ways to set up and execute their life science data analyses in a collaborative, extensible, open-source cloud environment. This panel discussion is brought to you by MetaCell and scientists from leading universities who share their experiences of advanced analysis and collaborative learning through the Cloud.

This brief video provides an introduction to the third session of INCF's Neuroinformatics Assembly 2023, focusing on how to streamling cross-platform data integration in a neuroscientific context. 

This talk describes the challenges to sustained operability and success of consortia, why many of these groups flounder after just a few years, and what steps can be taken to mitigate such outcomes. 

This talk discusses the BRAIN Initiative Cell Atlas Network (BICAN), taking a look specifically at how this network approaches the design, development, and maintenance of specimen and sequencing library portals. 

In this talk, you will hear about the challenges and costs of being FAIR in the many scientific fields, as well as opportunities to transform the ecology of the academic crediting system. 

This brief talk describes the challenge of global data sharing and governance, as well as efforts of the the Brain Research International Data Governance & Exchange (BRIDGE) to develop ready-made workflows to share data globally. 

This talk describes how to use DataLad for your data management and curation techniques  when dealing with animal datasets, which often contain several disparate types of data, including MRI, microscopy, histology, electrocorticography, and behavioral measurements. 

This brief talk covers an analysis technique for multi-band, multi-echo fMRI data, applying a denoising framework which can be used in an automated pipeline.  

This lightning talk describes an automated pipline for positron emission tomography (PET) data. 

This lecture goes into detailed description of how to process workflows in the virtual research environment (VRE), including approaches for standardization, metadata, containerization, and constructing and maintaining scientific pipelines. 

This lesson gives a quick introduction to the rest of this course, Research Workflows for Collaborative Neuroscience. 

This lesson provides an overview of how to conceptualize, design, implement, and maintain neuroscientific pipelines in via the cloud-based computational reproducibility platform Code Ocean. 

This lesson provides an overview of how to construct computational pipelines for neurophysiological data using DataJoint.

This talk describes approaches to maintaining integrated workflows and data management schema, taking advantage of the many open source, collaborative platforms already existing.